.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_errors_onnxruntime.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_errors_onnxruntime.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_errors_onnxruntime.py:


.. _l-errors-onnxruntime:

Errors with onnxruntime
=======================

Many mistakes might happen with *onnxruntime*.
This example looks into several common situations
in which *onnxruntime* does not return the model
prediction but raises an exception instead.
It starts by loading a model
(see :ref:`l-rf-iris-example`).
which produces a logistic regression
trained on *Iris* datasets. The model takes
a vector of dimension 2 and returns a class among three.

.. GENERATED FROM PYTHON SOURCE LINES 20-47

.. code-block:: default

    import skl2onnx
    import onnx
    import sklearn
    import onnxruntime as rt
    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.linear_model import LogisticRegression
    try:
        from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument
    except ImportError:
        # onnxruntime <= 0.5
        InvalidArgument = RuntimeError

    data = load_iris()
    clr = LogisticRegression().fit(data.data[:, :2], data.target)
    with open("logreg_iris.onnx", "wb") as f:
        f.write(
            skl2onnx.to_onnx(
                clr, data.data[:, :2].astype(np.float32),
                target_opset=12).SerializeToString())

    example2 = "logreg_iris.onnx"
    sess = rt.InferenceSession(example2)

    input_name = sess.get_inputs()[0].name
    output_name = sess.get_outputs()[0].name








.. GENERATED FROM PYTHON SOURCE LINES 48-51

The first example fails due to *bad types*.
*onnxruntime* only expects single floats (4 bytes)
and cannot handle any other kind of floats.

.. GENERATED FROM PYTHON SOURCE LINES 51-60

.. code-block:: default


    try:
        x = np.array([[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]],
                     dtype=np.float64)
        sess.run([output_name], {input_name: x})
    except Exception as e:
        print("Unexpected type")
        print("{0}: {1}".format(type(e), e))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Unexpected type
    <class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Unexpected input data type. Actual: (tensor(double)) , expected: (tensor(float))




.. GENERATED FROM PYTHON SOURCE LINES 61-63

The model fails to return an output if the name
is misspelled.

.. GENERATED FROM PYTHON SOURCE LINES 63-71

.. code-block:: default


    try:
        x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
        sess.run(["misspelled"], {input_name: x})
    except Exception as e:
        print("Misspelled output name")
        print("{0}: {1}".format(type(e), e))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Misspelled output name
    <class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Output Name:misspelled




.. GENERATED FROM PYTHON SOURCE LINES 72-74

The output name is optional, it can be replaced by *None*
and *onnxruntime* will then return all the outputs.

.. GENERATED FROM PYTHON SOURCE LINES 74-80

.. code-block:: default


    x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
    res = sess.run(None, {input_name: x})
    print("All outputs")
    print(res)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    All outputs
    [array([0, 0, 0], dtype=int64), [{0: 0.9999734163284302, 1: 2.656836477399338e-05, 2: 5.484377840758725e-09}, {0: 0.9999914169311523, 1: 8.446793799521402e-06, 2: 1.7366836857490853e-07}, {0: 0.9999918341636658, 1: 2.6854097541217925e-06, 2: 5.499288818100467e-06}]]




.. GENERATED FROM PYTHON SOURCE LINES 81-82

The same goes if the input name is misspelled.

.. GENERATED FROM PYTHON SOURCE LINES 82-90

.. code-block:: default


    try:
        x = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=np.float32)
        sess.run([output_name], {"misspelled": x})
    except Exception as e:
        print("Misspelled input name")
        print("{0}: {1}".format(type(e), e))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Misspelled input name
    <class 'onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument'>: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid Feed Input Name:misspelled




.. GENERATED FROM PYTHON SOURCE LINES 91-93

*onnxruntime* does not necessarily fail if the input
dimension is a multiple of the expected input dimension.

.. GENERATED FROM PYTHON SOURCE LINES 93-119

.. code-block:: default


    for x in [
            np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
            np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32),
            np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
            np.array([1.0, 2.0, 3.0], dtype=np.float32),
            np.array([[1.0, 2.0, 3.0]], dtype=np.float32)]:
        try:
            r = sess.run([output_name], {input_name: x})
            print("Shape={0} and predicted labels={1}".format(x.shape, r))
        except (RuntimeError, InvalidArgument) as e:
            print("Shape={0} and error={1}".format(x.shape, e))

    for x in [
            np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32),
            np.array([[1.0, 2.0, 3.0, 4.0]], dtype=np.float32),
            np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32),
            np.array([1.0, 2.0, 3.0], dtype=np.float32),
            np.array([[1.0, 2.0, 3.0]], dtype=np.float32)]:
        try:
            r = sess.run(None, {input_name: x})
            print("Shape={0} and predicted probabilities={1}".format(
                x.shape, r[1]))
        except (RuntimeError, InvalidArgument) as e:
            print("Shape={0} and error={1}".format(x.shape, e))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Shape=(4,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs or the model.
    Shape=(1, 4) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
     index: 1 Got: 4 Expected: 2
     Please fix either the inputs or the model.
    Shape=(2, 2) and predicted labels=[array([0, 0], dtype=int64)]
    Shape=(3,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs or the model.
    Shape=(1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
     index: 1 Got: 3 Expected: 2
     Please fix either the inputs or the model.
    Shape=(4,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs or the model.
    Shape=(1, 4) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
     index: 1 Got: 4 Expected: 2
     Please fix either the inputs or the model.
    Shape=(2, 2) and predicted probabilities=[{0: 0.9999734163284302, 1: 2.656836477399338e-05, 2: 5.484377840758725e-09}, {0: 0.9999914169311523, 1: 8.446793799521402e-06, 2: 1.7366836857490853e-07}]
    Shape=(3,) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 1 Expected: 2 Please fix either the inputs or the model.
    Shape=(1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: X for the following indices
     index: 1 Got: 3 Expected: 2
     Please fix either the inputs or the model.




.. GENERATED FROM PYTHON SOURCE LINES 120-122

It does not fail either if the number of dimension
is higher than expects but produces a warning.

.. GENERATED FROM PYTHON SOURCE LINES 122-133

.. code-block:: default


    for x in [
            np.array([[[1.0, 2.0], [3.0, 4.0]]], dtype=np.float32),
            np.array([[[1.0, 2.0, 3.0]]], dtype=np.float32),
            np.array([[[1.0, 2.0]], [[3.0, 4.0]]], dtype=np.float32)]:
        try:
            r = sess.run([output_name], {input_name: x})
            print("Shape={0} and predicted labels={1}".format(x.shape, r))
        except (RuntimeError, InvalidArgument) as e:
            print("Shape={0} and error={1}".format(x.shape, e))





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Shape=(1, 2, 2) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs or the model.
    Shape=(1, 1, 3) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs or the model.
    Shape=(2, 1, 2) and error=[ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Invalid rank for input: X Got: 3 Expected: 2 Please fix either the inputs or the model.




.. GENERATED FROM PYTHON SOURCE LINES 134-135

**Versions used for this example**

.. GENERATED FROM PYTHON SOURCE LINES 135-141

.. code-block:: default


    print("numpy:", np.__version__)
    print("scikit-learn:", sklearn.__version__)
    print("onnx: ", onnx.__version__)
    print("onnxruntime: ", rt.__version__)
    print("skl2onnx: ", skl2onnx.__version__)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    numpy: 1.23.5
    scikit-learn: 1.2.2
    onnx:  1.13.1
    onnxruntime:  1.14.1
    skl2onnx:  1.14.0





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.159 seconds)


.. _sphx_glr_download_auto_examples_plot_errors_onnxruntime.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_errors_onnxruntime.py <plot_errors_onnxruntime.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_errors_onnxruntime.ipynb <plot_errors_onnxruntime.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_